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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see .
*/
/*
* DecisionTableHashKey.java
* Copyright (C) 2007-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.rules;
import java.io.Serializable;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
/**
* Class providing hash table keys for DecisionTable
*/
public class DecisionTableHashKey
implements Serializable, RevisionHandler {
/** for serialization */
static final long serialVersionUID = 5674163500154964602L;
/** Array of attribute values for an instance */
private double [] attributes;
/** True for an index if the corresponding attribute value is missing. */
private boolean [] missing;
/** The key */
private int key;
/**
* Constructor for a hashKey
*
* @param t an instance from which to generate a key
* @param numAtts the number of attributes
* @param ignoreClass if true treat the class as a normal attribute
* @throws Exception if something goes wrong
*/
public DecisionTableHashKey(Instance t, int numAtts, boolean ignoreClass) throws Exception {
int i;
int cindex = t.classIndex();
key = -999;
attributes = new double [numAtts];
missing = new boolean [numAtts];
for (i=0;i
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